Amogh-2404/LegalGPT

Model Card for Model ID

Fine tuned on Indian Legal domain, LegalGPT is your personal legal assistant.

Model Details

Model Description

• Developed by: R.Amogh
• Funded by [optional]: [More Information Needed]
• Shared by [optional]: [More Information Needed]
• Model type: [More Information Needed]
• Language(s) (NLP): [More Information Needed]
• License: [More Information Needed]
• Finetuned from model [optional]: [More Information Needed]

Model Sources [optional]

• Repository: [More Information Needed]
• Paper [optional]: [More Information Needed]
• Demo [optional]: [More Information Needed]

How to use

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_model = "facebook/opt-350m"  # <- BASE MODEL NAME HERE
adapter_repo = "Amogh-2404/LegalGPT"

model = AutoModelForCausalLM.from_pretrained(base_model)
tokenizer = AutoTokenizer.from_pretrained(adapter_repo)
model = PeftModel.from_pretrained(model, adapter_repo)

Uses

Direct Use

Downstream Use [optional]

Out-of-Scope Use

Bias, Risks, and Limitations

Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_model = "facebook/opt-350m"  # <- BASE MODEL NAME HERE
adapter_repo = "Amogh-2404/LegalGPT"

model = AutoModelForCausalLM.from_pretrained(base_model)
tokenizer = AutoTokenizer.from_pretrained(adapter_repo)
model = PeftModel.from_pretrained(model, adapter_repo)

Training Details

Training Data

Training Procedure

Preprocessing [optional]

Training Hyperparameters

• Training regime: fp16 mixed precision

Speeds, Sizes, Times [optional]

Evaluation

Testing Data, Factors & Metrics

Testing Data

Factors

Metrics

Results

Summary

Model Examination [optional]

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

• Hardware Type: [More Information Needed]
• Hours used: [More Information Needed]
• Cloud Provider: [More Information Needed]
• Compute Region: [More Information Needed]
• Carbon Emitted: [More Information Needed]

Technical Specifications [optional]

Model Architecture and Objective

Compute Infrastructure

Hardware

Software

Citation [optional]

BibTeX:

APA:

Glossary [optional]

More Information [optional]

Model Card Authors [optional]

Model Card Contact

Framework versions

• PEFT 0.11.1